A Model for Personalized Keyword Extraction from Web Pages using Segmentation

TL;DR

This paper introduces a personalized keyword extraction model from web pages that uses segmentation to improve relevance, validated through empirical experiments demonstrating its effectiveness.

Contribution

It presents a novel segmentation-based approach for personalized keyword extraction from web pages, enhancing customization in web content retrieval.

Findings

The model effectively extracts personalized keywords.

Segmentation improves keyword relevance.

Experimental validation confirms efficiency.

Abstract

The World Wide Web caters to the needs of billions of users in heterogeneous groups. Each user accessing the World Wide Web might have his / her own specific interest and would expect the web to respond to the specific requirements. The process of making the web to react in a customized manner is achieved through personalization. This paper proposes a novel model for extracting keywords from a web page with personalization being incorporated into it. The keyword extraction problem is approached with the help of web page segmentation which facilitates in making the problem simpler and solving it effectively. The proposed model is implemented as a prototype and the experiments conducted on it empirically validate the model's efficiency.